Inspiration
We wanted to build something cool with Machine Learning and D3.js. Using a Kaggle Dataset for this purpose seemed the best way to go.
What it does
We try to tell how movies have evolved over the past 100 years. We also implemented a Machine Learning module that predicts movie revenues based on Director, Plot, Actor and Budget.
How we built it
The backend was written in Python, interfaced using Flask with the JS front end. We cleaned our dataset using Pandas. Built the JS visualisations with d3.js.
Challenges we ran into
It was a task identifying the correct model to use. Moreover, understanding Pandas and its neat tricks was another task. Additionally, d3.js had a complicated chaining syntax.
Accomplishments that we're proud of
Setting up Flask. Learning Javascript and D3.js. Data Analytics. Implementing machine learning models.
What we learned
Don't give up. Team work. One Hot Encoding and Dimensionality Curse. Flask, Numpy, Pandas.
What's next for AIMLDB
Using more sophisticated techniques and feature selection to predict better revenues.
We also got ourselves a domain - aimldb.com, but domain.com let us down. This is a wordplay on using AI/ML techniques to predict revenue from the iMDB database.
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